A significant number of hotel bookings are called off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.
The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.
The cancellation of bookings impact a hotel on various fronts:
The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.
Data Dictionary
# this will help in making the Python code more structured automatically (help adhere to good coding practices)
# %load_ext nb_black
import warnings
warnings.filterwarnings("ignore")
from statsmodels.tools.sm_exceptions import ConvergenceWarning
warnings.simplefilter("ignore", ConvergenceWarning)
# Libraries to help with reading and manipulating data
import pandas as pd
import numpy as np
# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns
# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# setting the precision of floating numbers to 5 decimal points
pd.set_option("display.float_format", lambda x: "%.5f" % x)
# Library to split data
from sklearn.model_selection import train_test_split
# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
# To tune different models
from sklearn.model_selection import GridSearchCV
# To get diferent metric scores
from sklearn.metrics import (
f1_score,
accuracy_score,
recall_score,
precision_score,
confusion_matrix,
roc_auc_score,
ConfusionMatrixDisplay,
precision_recall_curve,
roc_curve,
make_scorer,
)
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
hotel = pd.read_csv('/content/drive/MyDrive/PGP-DSBA/INNHotelsGroup.csv') ## Fill the blank to read the data
# copying data to another variable to avoid any changes to original data
data = hotel.copy()
data.head()
| Booking_ID | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | INN00001 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 224 | 2017 | 10 | 2 | Offline | 0 | 0 | 0 | 65.00000 | 0 | Not_Canceled |
| 1 | INN00002 | 2 | 0 | 2 | 3 | Not Selected | 0 | Room_Type 1 | 5 | 2018 | 11 | 6 | Online | 0 | 0 | 0 | 106.68000 | 1 | Not_Canceled |
| 2 | INN00003 | 1 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2018 | 2 | 28 | Online | 0 | 0 | 0 | 60.00000 | 0 | Canceled |
| 3 | INN00004 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 211 | 2018 | 5 | 20 | Online | 0 | 0 | 0 | 100.00000 | 0 | Canceled |
| 4 | INN00005 | 2 | 0 | 1 | 1 | Not Selected | 0 | Room_Type 1 | 48 | 2018 | 4 | 11 | Online | 0 | 0 | 0 | 94.50000 | 0 | Canceled |
data.tail()
| Booking_ID | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36270 | INN36271 | 3 | 0 | 2 | 6 | Meal Plan 1 | 0 | Room_Type 4 | 85 | 2018 | 8 | 3 | Online | 0 | 0 | 0 | 167.80000 | 1 | Not_Canceled |
| 36271 | INN36272 | 2 | 0 | 1 | 3 | Meal Plan 1 | 0 | Room_Type 1 | 228 | 2018 | 10 | 17 | Online | 0 | 0 | 0 | 90.95000 | 2 | Canceled |
| 36272 | INN36273 | 2 | 0 | 2 | 6 | Meal Plan 1 | 0 | Room_Type 1 | 148 | 2018 | 7 | 1 | Online | 0 | 0 | 0 | 98.39000 | 2 | Not_Canceled |
| 36273 | INN36274 | 2 | 0 | 0 | 3 | Not Selected | 0 | Room_Type 1 | 63 | 2018 | 4 | 21 | Online | 0 | 0 | 0 | 94.50000 | 0 | Canceled |
| 36274 | INN36275 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 207 | 2018 | 12 | 30 | Offline | 0 | 0 | 0 | 161.67000 | 0 | Not_Canceled |
data.shape
(36275, 19)
The dataset has 36,275 rows and 19 columns.
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 36275 entries, 0 to 36274 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Booking_ID 36275 non-null object 1 no_of_adults 36275 non-null int64 2 no_of_children 36275 non-null int64 3 no_of_weekend_nights 36275 non-null int64 4 no_of_week_nights 36275 non-null int64 5 type_of_meal_plan 36275 non-null object 6 required_car_parking_space 36275 non-null int64 7 room_type_reserved 36275 non-null object 8 lead_time 36275 non-null int64 9 arrival_year 36275 non-null int64 10 arrival_month 36275 non-null int64 11 arrival_date 36275 non-null int64 12 market_segment_type 36275 non-null object 13 repeated_guest 36275 non-null int64 14 no_of_previous_cancellations 36275 non-null int64 15 no_of_previous_bookings_not_canceled 36275 non-null int64 16 avg_price_per_room 36275 non-null float64 17 no_of_special_requests 36275 non-null int64 18 booking_status 36275 non-null object dtypes: float64(1), int64(13), object(5) memory usage: 5.3+ MB
The dataset consists of several data types:
# checking for duplicate values
data.duplicated().sum()
0
There are no duplicate values.
# checking for missing values
data.isnull().sum()
Booking_ID 0 no_of_adults 0 no_of_children 0 no_of_weekend_nights 0 no_of_week_nights 0 type_of_meal_plan 0 required_car_parking_space 0 room_type_reserved 0 lead_time 0 arrival_year 0 arrival_month 0 arrival_date 0 market_segment_type 0 repeated_guest 0 no_of_previous_cancellations 0 no_of_previous_bookings_not_canceled 0 avg_price_per_room 0 no_of_special_requests 0 booking_status 0 dtype: int64
There are no missing values.
Let's drop the Booking_ID column first before we proceed forward.
data = data.drop(['Booking_ID'], axis = 1)
data.head()
| no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 224 | 2017 | 10 | 2 | Offline | 0 | 0 | 0 | 65.00000 | 0 | Not_Canceled |
| 1 | 2 | 0 | 2 | 3 | Not Selected | 0 | Room_Type 1 | 5 | 2018 | 11 | 6 | Online | 0 | 0 | 0 | 106.68000 | 1 | Not_Canceled |
| 2 | 1 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2018 | 2 | 28 | Online | 0 | 0 | 0 | 60.00000 | 0 | Canceled |
| 3 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 211 | 2018 | 5 | 20 | Online | 0 | 0 | 0 | 100.00000 | 0 | Canceled |
| 4 | 2 | 0 | 1 | 1 | Not Selected | 0 | Room_Type 1 | 48 | 2018 | 4 | 11 | Online | 0 | 0 | 0 | 94.50000 | 0 | Canceled |
Let's check the statistical summary of the data.
data.describe(include = 'all').T
| count | unique | top | freq | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| no_of_adults | 36275.00000 | NaN | NaN | NaN | 1.84496 | 0.51871 | 0.00000 | 2.00000 | 2.00000 | 2.00000 | 4.00000 |
| no_of_children | 36275.00000 | NaN | NaN | NaN | 0.10528 | 0.40265 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 10.00000 |
| no_of_weekend_nights | 36275.00000 | NaN | NaN | NaN | 0.81072 | 0.87064 | 0.00000 | 0.00000 | 1.00000 | 2.00000 | 7.00000 |
| no_of_week_nights | 36275.00000 | NaN | NaN | NaN | 2.20430 | 1.41090 | 0.00000 | 1.00000 | 2.00000 | 3.00000 | 17.00000 |
| type_of_meal_plan | 36275 | 4 | Meal Plan 1 | 27835 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| required_car_parking_space | 36275.00000 | NaN | NaN | NaN | 0.03099 | 0.17328 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 |
| room_type_reserved | 36275 | 7 | Room_Type 1 | 28130 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| lead_time | 36275.00000 | NaN | NaN | NaN | 85.23256 | 85.93082 | 0.00000 | 17.00000 | 57.00000 | 126.00000 | 443.00000 |
| arrival_year | 36275.00000 | NaN | NaN | NaN | 2017.82043 | 0.38384 | 2017.00000 | 2018.00000 | 2018.00000 | 2018.00000 | 2018.00000 |
| arrival_month | 36275.00000 | NaN | NaN | NaN | 7.42365 | 3.06989 | 1.00000 | 5.00000 | 8.00000 | 10.00000 | 12.00000 |
| arrival_date | 36275.00000 | NaN | NaN | NaN | 15.59700 | 8.74045 | 1.00000 | 8.00000 | 16.00000 | 23.00000 | 31.00000 |
| market_segment_type | 36275 | 5 | Online | 23214 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| repeated_guest | 36275.00000 | NaN | NaN | NaN | 0.02564 | 0.15805 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 |
| no_of_previous_cancellations | 36275.00000 | NaN | NaN | NaN | 0.02335 | 0.36833 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 13.00000 |
| no_of_previous_bookings_not_canceled | 36275.00000 | NaN | NaN | NaN | 0.15341 | 1.75417 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 58.00000 |
| avg_price_per_room | 36275.00000 | NaN | NaN | NaN | 103.42354 | 35.08942 | 0.00000 | 80.30000 | 99.45000 | 120.00000 | 540.00000 |
| no_of_special_requests | 36275.00000 | NaN | NaN | NaN | 0.61966 | 0.78624 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 5.00000 |
| booking_status | 36275 | 2 | Not_Canceled | 24390 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (15,10))
kde: whether to show the density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a triangle will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
histogram_boxplot(data, "lead_time")
histogram_boxplot(data, 'avg_price_per_room')
data[data["avg_price_per_room"] == 0]
| no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 63 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 2 | 2017 | 9 | 10 | Complementary | 0 | 0 | 0 | 0.00000 | 1 | Not_Canceled |
| 145 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 13 | 2018 | 6 | 1 | Complementary | 1 | 3 | 5 | 0.00000 | 1 | Not_Canceled |
| 209 | 1 | 0 | 0 | 0 | Meal Plan 1 | 0 | Room_Type 1 | 4 | 2018 | 2 | 27 | Complementary | 0 | 0 | 0 | 0.00000 | 1 | Not_Canceled |
| 266 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2017 | 8 | 12 | Complementary | 1 | 0 | 1 | 0.00000 | 1 | Not_Canceled |
| 267 | 1 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 4 | 2017 | 8 | 23 | Complementary | 0 | 0 | 0 | 0.00000 | 1 | Not_Canceled |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 35983 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 7 | 0 | 2018 | 6 | 7 | Complementary | 1 | 4 | 17 | 0.00000 | 1 | Not_Canceled |
| 36080 | 1 | 0 | 1 | 1 | Meal Plan 1 | 0 | Room_Type 7 | 0 | 2018 | 3 | 21 | Complementary | 1 | 3 | 15 | 0.00000 | 1 | Not_Canceled |
| 36114 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2018 | 3 | 2 | Online | 0 | 0 | 0 | 0.00000 | 0 | Not_Canceled |
| 36217 | 2 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 2 | 3 | 2017 | 8 | 9 | Online | 0 | 0 | 0 | 0.00000 | 2 | Not_Canceled |
| 36250 | 1 | 0 | 0 | 2 | Meal Plan 2 | 0 | Room_Type 1 | 6 | 2017 | 12 | 10 | Online | 0 | 0 | 0 | 0.00000 | 0 | Not_Canceled |
545 rows × 18 columns
There 545 etries where the average price per was 0 euros.
data.loc[data["avg_price_per_room"] == 0, "market_segment_type"].value_counts()
Complementary 354 Online 191 Name: market_segment_type, dtype: int64
Out of the 545 entries where the average price per was 0 euros, 354 of them were of the complementary market segment and 191 of them were of the online market segment.
# Calculating the 25th quantile
Q1 = data["avg_price_per_room"].quantile(0.25)
# Calculating the 75th quantile
Q3 = data["avg_price_per_room"].quantile(0.75)
# Calculating IQR
IQR = Q3 - Q1
# Calculating value of upper whisker
Upper_Whisker = Q3 + 1.5 * IQR
Upper_Whisker
179.55
The upper whisker for the distribution of average price per room is 179.55 euros.
# assigning the outliers the value of upper whisker
data.loc[data["avg_price_per_room"] >= 500, "avg_price_per_room"] = Upper_Whisker
histogram_boxplot(data, 'no_of_previous_cancellations')
Many people did not cancel there bookings.
histogram_boxplot(data, 'no_of_previous_bookings_not_canceled') ## Complete the code to create histogram_boxplot for number of previous booking not canceled
Many people decided to stick with their bookings.
# function to create labeled barplots
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 2, 6))
else:
plt.figure(figsize=(n + 2, 6))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n],
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
plt.show() # show the plot
labeled_barplot(data, "no_of_adults", perc=True)
Majority of the bookings was for 2 adults (72%).
labeled_barplot(data, 'no_of_children', perc=True)
Many of the bookings had no children (92.6%).
# replacing 9, and 10 children with 3
data["no_of_children"] = data["no_of_children"].replace([9, 10], 3)
labeled_barplot(data,'no_of_week_nights',perc=True)
Most people made bookings for two week nights (31.5%)
labeled_barplot(data,'no_of_weekend_nights',perc=True)
Many people made bookings that were not over the weekend (46.5%).
labeled_barplot(data,'required_car_parking_space',perc=True)
Majority of the bookings did not have a required car parking space (96.9%).
labeled_barplot(data,'type_of_meal_plan', perc=True)
Majority of the customers chose meal plan 1 (76.7%).
labeled_barplot(data,'room_type_reserved',perc=True)
Majority of the customers choose room type 1 (77.5%).
labeled_barplot(data,'arrival_month',perc=True)
The busiest months for bookings were October (14.7%), September (12.7%), and August (10.5%).
labeled_barplot(data,'market_segment_type',perc=True)
Most of the guests come from the online market segment (64%).
labeled_barplot(data,'no_of_special_requests',perc=True)
Many people did not make any special requests (54.5%).
labeled_barplot(data,'booking_status',perc=True)
Majority of the bookings were not canceled (67.2%).
Encode Canceled bookings to 1 and Not_Canceled as 0 for further analysis
data["booking_status"] = data["booking_status"].apply(
lambda x: 1 if x == "Canceled" else 0
)
cols_list = data.select_dtypes(include=np.number).columns.tolist()
plt.figure(figsize=(12, 7))
sns.heatmap(
data[cols_list].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.show()
Creating functions that will help us with further analysis.
### function to plot distributions wrt target
def distribution_plot_wrt_target(data, predictor, target):
fig, axs = plt.subplots(2, 2, figsize=(12, 10))
target_uniq = data[target].unique()
axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
sns.histplot(
data=data[data[target] == target_uniq[0]],
x=predictor,
kde=True,
ax=axs[0, 0],
color="teal",
stat="density",
)
axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
sns.histplot(
data=data[data[target] == target_uniq[1]],
x=predictor,
kde=True,
ax=axs[0, 1],
color="orange",
stat="density",
)
axs[1, 0].set_title("Boxplot w.r.t target")
sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")
axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
sns.boxplot(
data=data,
x=target,
y=predictor,
ax=axs[1, 1],
showfliers=False,
palette="gist_rainbow",
)
plt.tight_layout()
plt.show()
def stacked_barplot(data, predictor, target):
"""
Print the category counts and plot a stacked bar chart
data: dataframe
predictor: independent variable
target: target variable
"""
count = data[predictor].nunique()
sorter = data[target].value_counts().index[-1]
tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
by=sorter, ascending=False
)
print(tab1)
print("-" * 120)
tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
by=sorter, ascending=False
)
tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
plt.legend(
loc="lower left", frameon=False,
)
plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
plt.show()
Hotel rates are dynamic and change according to demand and customer demographics. Let's see how prices vary across different market segments
plt.figure(figsize=(10, 6))
sns.boxplot(
data=data, x="market_segment_type", y="avg_price_per_room", palette="gist_rainbow"
)
plt.show()
It appears the the online market segment had the highest median price per room and the complementary market segment had the lowest pricce per room.
Let's see how booking status varies across different market segments. Also, how average price per room impacts booking status
stacked_barplot(data, "market_segment_type", "booking_status")
booking_status 0 1 All market_segment_type All 24390 11885 36275 Online 14739 8475 23214 Offline 7375 3153 10528 Corporate 1797 220 2017 Aviation 88 37 125 Complementary 391 0 391 ------------------------------------------------------------------------------------------------------------------------
The online market segment had the most cancellations whereas the complementary market segment had no cancellations.
Many guests have special requirements when booking a hotel room. Let's see how it impacts cancellations
stacked_barplot(data,'no_of_special_requests', 'booking_status')
booking_status 0 1 All no_of_special_requests All 24390 11885 36275 0 11232 8545 19777 1 8670 2703 11373 2 3727 637 4364 3 675 0 675 4 78 0 78 5 8 0 8 ------------------------------------------------------------------------------------------------------------------------
The most amount of cancellations happened when there were no special requests made. However, There were no cancellations when 3 or more special requests were made.
Let's see if the special requests made by the customers impacts the prices of a room
plt.figure(figsize=(10, 5))
sns.boxplot(data=data, x='no_of_special_requests', y='avg_price_per_room')
plt.show()
The distribution for average price per room seems similar for the different amounts of special requests.
We saw earlier that there is a positive correlation between booking status and average price per room. Let's analyze it
distribution_plot_wrt_target(data, "avg_price_per_room", "booking_status")
The average price per room for cancelled bookings is around 110 euros and the average price per room of bookings that were not cancelled was around 90 euros.
There is a positive correlation between booking status and lead time also. Let's analyze it further
distribution_plot_wrt_target(data,'lead_time','booking_status')
The average lead time for cancelled bookings is around 130 days and the average lead time of bookings that were not cancelled was around 40 days.
Generally people travel with their spouse and children for vacations or other activities. Let's create a new dataframe of the customers who traveled with their families and analyze the impact on booking status.
family_data = data[(data["no_of_children"] >= 0) & (data["no_of_adults"] > 1)]
family_data.shape
(28441, 18)
There are 28,441 customers that travelled with families.
family_data["no_of_family_members"] = (
family_data["no_of_adults"] + family_data["no_of_children"]
)
stacked_barplot(family_data,'no_of_family_members','booking_status')
booking_status 0 1 All no_of_family_members All 18456 9985 28441 2 15506 8213 23719 3 2425 1368 3793 4 514 398 912 5 11 6 17 ------------------------------------------------------------------------------------------------------------------------
The most amount of cancellations occured when there were 4 family members.
Let's do a similar analysis for the customer who stay for at least a day at the hotel.
stay_data = data[(data["no_of_week_nights"] > 0) & (data["no_of_weekend_nights"] > 0)]
stay_data.shape
(17094, 18)
17,094 customers stayed for at least a day in the hotel.
stay_data["total_days"] = (
stay_data["no_of_week_nights"] + stay_data["no_of_weekend_nights"]
)
stacked_barplot(stay_data, 'total_days','booking_status') ## Complete the code to plot stacked barplot for total days and booking status
booking_status 0 1 All total_days All 10979 6115 17094 3 3689 2183 5872 4 2977 1387 4364 5 1593 738 2331 2 1301 639 1940 6 566 465 1031 7 590 383 973 8 100 79 179 10 51 58 109 9 58 53 111 14 5 27 32 15 5 26 31 13 3 15 18 12 9 15 24 11 24 15 39 20 3 8 11 19 1 5 6 16 1 5 6 17 1 4 5 18 0 3 3 21 1 3 4 22 0 2 2 23 1 1 2 24 0 1 1 ------------------------------------------------------------------------------------------------------------------------
Generally, the fewer amount of total days stayed in a hotel, the fewer cancellations.
Repeating guests are the guests who stay in the hotel often and are important to brand equity. Let's see what percentage of repeating guests cancel?
stacked_barplot(data,'repeated_guest','booking_status')
booking_status 0 1 All repeated_guest All 24390 11885 36275 0 23476 11869 35345 1 914 16 930 ------------------------------------------------------------------------------------------------------------------------
It appears that customers who are not repeated guest had more cancellations than those that were.
Let's find out what are the busiest months in the hotel.
# grouping the data on arrival months and extracting the count of bookings
monthly_data = data.groupby(["arrival_month"])["booking_status"].count()
# creating a dataframe with months and count of customers in each month
monthly_data = pd.DataFrame(
{"Month": list(monthly_data.index), "Guests": list(monthly_data.values)}
)
# plotting the trend over different months
plt.figure(figsize=(10, 5))
sns.lineplot(data=monthly_data, x="Month", y="Guests")
plt.show()
The busiest months appear to be August, September, and October
Let's check the percentage of bookings canceled in each month.
stacked_barplot(data,'arrival_month','booking_status')
booking_status 0 1 All arrival_month All 24390 11885 36275 10 3437 1880 5317 9 3073 1538 4611 8 2325 1488 3813 7 1606 1314 2920 6 1912 1291 3203 4 1741 995 2736 5 1650 948 2598 11 2105 875 2980 3 1658 700 2358 2 1274 430 1704 12 2619 402 3021 1 990 24 1014 ------------------------------------------------------------------------------------------------------------------------
The most amount of cancellations occurred in July and the least amount of cancellations occurred in January.
As hotel room prices are dynamic, Let's see how the prices vary across different months
plt.figure(figsize=(10, 5))
sns.lineplot(data=data, x='arrival_month', y='avg_price_per_room')
plt.show()
The price per room seem to be lower at the beginning and end of the year. There is an increase in average price per room in the middle of the year.
# outlier detection using boxplot
numeric_columns = data.select_dtypes(include=np.number).columns.tolist()
# dropping booking_status
numeric_columns.remove("booking_status")
plt.figure(figsize=(15, 12))
for i, variable in enumerate(numeric_columns):
plt.subplot(4, 4, i + 1)
plt.boxplot(data[variable], whis=1.5)
plt.tight_layout()
plt.title(variable)
plt.show()
# functions to treat outliers by flooring and capping
def treat_outliers(df, col):
"""
Treats outliers in a variable
df: dataframe
col: dataframe column
"""
Q1 = df[col].quantile(0.25) # 25th quantile
Q3 = df[col].quantile(0.75) # 75th quantile
IQR = Q3 - Q1
Lower_Whisker = Q1 - 1.5 * IQR
Upper_Whisker = Q3 + 1.5 * IQR
# all the values smaller than Lower_Whisker will be assigned the value of Lower_Whisker
# all the values greater than Upper_Whisker will be assigned the value of Upper_Whisker
df[col] = np.clip(df[col], Lower_Whisker, Upper_Whisker)
return df
def treat_outliers_all(df, col_list):
"""
Treat outliers in a list of variables
df: dataframe
col_list: list of dataframe columns
"""
for c in col_list:
df = treat_outliers(df, c)
return df
# list of columns to treat for outliers
treat_out_cols = ["lead_time", "avg_price_per_room", "no_of_week_nights", "no_of_weekend_nights"]
# create a new data frame after treating outliers in the colums
data = treat_outliers_all(data, treat_out_cols)
# outlier detection using boxplot
numeric_columns = data.select_dtypes(include=np.number).columns.tolist()
plt.figure(figsize=(15, 15))
for i, variable in enumerate(numeric_columns):
plt.subplot(5, 4, i + 1)
plt.boxplot(data[variable], whis=1.5)
plt.tight_layout()
plt.title(variable)
plt.show()
Both the cases are important as:
If we predict that a booking will not be canceled and the booking gets canceled then the hotel will lose resources and will have to bear additional costs of distribution channels.
If we predict that a booking will get canceled and the booking doesn't get canceled the hotel might not be able to provide satisfactory services to the customer by assuming that this booking will be canceled. This might damage the brand equity.
F1 Score to be maximized, greater the F1 score higher are the chances of minimizing False Negatives and False Positives. # defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
model, predictors, target, threshold=0.5
):
"""
Function to compute different metrics to check classification model performance
model: classifier
predictors: independent variables
target: dependent variable
threshold: threshold for classifying the observation as class 1
"""
# checking which probabilities are greater than threshold
pred_temp = model.predict(predictors) > threshold
# rounding off the above values to get classes
pred = np.round(pred_temp)
acc = accuracy_score(target, pred) # to compute Accuracy
recall = recall_score(target, pred) # to compute Recall
precision = precision_score(target, pred) # to compute Precision
f1 = f1_score(target, pred) # to compute F1-score
# creating a dataframe of metrics
df_perf = pd.DataFrame(
{"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
index=[0],
)
return df_perf
# defining a function to plot the confusion_matrix of a classification model
def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
"""
To plot the confusion_matrix with percentages
model: classifier
predictors: independent variables
target: dependent variable
threshold: threshold for classifying the observation as class 1
"""
y_pred = model.predict(predictors) > threshold
cm = confusion_matrix(target, y_pred)
labels = np.asarray(
[
["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
for item in cm.flatten()
]
).reshape(2, 2)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=labels, fmt="")
plt.ylabel("True label")
plt.xlabel("Predicted label")
X = data.drop(["booking_status"], axis=1)
Y = data["booking_status"]
# adding constant
X = sm.add_constant(X)
# get dummy variables
X = pd.get_dummies(X, drop_first=True)
# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.30, random_state=1)
print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))
Shape of Training set : (25392, 28) Shape of test set : (10883, 28) Percentage of classes in training set: 0 0.67064 1 0.32936 Name: booking_status, dtype: float64 Percentage of classes in test set: 0 0.67638 1 0.32362 Name: booking_status, dtype: float64
# fitting logistic regression model
logit = sm.Logit(y_train, X_train.astype(float))
lg = logit.fit(disp=False)
print(lg.summary())
Logit Regression Results
==============================================================================
Dep. Variable: booking_status No. Observations: 25392
Model: Logit Df Residuals: 25364
Method: MLE Df Model: 27
Date: Sun, 18 Jun 2023 Pseudo R-squ.: 0.3298
Time: 05:50:48 Log-Likelihood: -10785.
converged: False LL-Null: -16091.
Covariance Type: nonrobust LLR p-value: 0.000
========================================================================================================
coef std err z P>|z| [0.025 0.975]
--------------------------------------------------------------------------------------------------------
const -985.5063 121.110 -8.137 0.000 -1222.878 -748.135
no_of_adults 0.1113 0.038 2.949 0.003 0.037 0.185
no_of_children 0.1696 0.062 2.738 0.006 0.048 0.291
no_of_weekend_nights 0.1114 0.020 5.665 0.000 0.073 0.150
no_of_week_nights 0.0128 0.014 0.946 0.344 -0.014 0.039
required_car_parking_space -1.6112 0.139 -11.626 0.000 -1.883 -1.340
lead_time 0.0164 0.000 59.676 0.000 0.016 0.017
arrival_year 0.4871 0.060 8.117 0.000 0.370 0.605
arrival_month -0.0397 0.006 -6.129 0.000 -0.052 -0.027
arrival_date 0.0008 0.002 0.400 0.689 -0.003 0.005
repeated_guest -2.2034 0.575 -3.829 0.000 -3.331 -1.076
no_of_previous_cancellations 0.2591 0.085 3.035 0.002 0.092 0.426
no_of_previous_bookings_not_canceled -0.1858 0.159 -1.170 0.242 -0.497 0.126
avg_price_per_room 0.0198 0.001 25.103 0.000 0.018 0.021
no_of_special_requests -1.4729 0.030 -48.829 0.000 -1.532 -1.414
type_of_meal_plan_Meal Plan 2 0.2497 0.065 3.819 0.000 0.122 0.378
type_of_meal_plan_Meal Plan 3 21.2193 2.2e+04 0.001 0.999 -4.3e+04 4.31e+04
type_of_meal_plan_Not Selected 0.2908 0.053 5.446 0.000 0.186 0.395
room_type_reserved_Room_Type 2 -0.3588 0.132 -2.727 0.006 -0.617 -0.101
room_type_reserved_Room_Type 3 -0.0523 1.317 -0.040 0.968 -2.633 2.529
room_type_reserved_Room_Type 4 -0.2877 0.054 -5.366 0.000 -0.393 -0.183
room_type_reserved_Room_Type 5 -0.7111 0.208 -3.414 0.001 -1.119 -0.303
room_type_reserved_Room_Type 6 -0.7080 0.149 -4.766 0.000 -0.999 -0.417
room_type_reserved_Room_Type 7 -0.7325 0.280 -2.618 0.009 -1.281 -0.184
market_segment_type_Complementary -28.2057 2.2e+04 -0.001 0.999 -4.31e+04 4.3e+04
market_segment_type_Corporate -1.2417 0.264 -4.698 0.000 -1.760 -0.724
market_segment_type_Offline -2.2625 0.253 -8.951 0.000 -2.758 -1.767
market_segment_type_Online -0.4728 0.249 -1.896 0.058 -0.962 0.016
========================================================================================================
Negative values of the coefficient show that the probability of a person cancelling their booking decreases with the increase of the corresponding attribute value.
Positive values of the coefficient show that the probability of a person cancelling their booking increases with the increase of the corresponding attribute value.
p-value of a variable indicates if the variable is significant or not. If we consider the significance level to be 0.05 (5%), then any variable with a p-value less than 0.05 would be considered significant.
print("Training performance:")
model_performance_classification_statsmodels(lg, X_train, y_train)
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80517 | 0.63446 | 0.73735 | 0.68205 |
confusion_matrix_statsmodels(lg, X_train, y_train)
The f1_score of the model is ~0.68 and we will try to maximize it further
The variables used to build the model might contain multicollinearity, which will affect the p-values
We will have to remove multicollinearity from the data to get reliable coefficients and p-values
# we will define a function to check VIF
def checking_vif(predictors):
vif = pd.DataFrame()
vif["feature"] = predictors.columns
# calculating VIF for each feature
vif["VIF"] = [
variance_inflation_factor(predictors.values, i)
for i in range(len(predictors.columns))
]
return vif
checking_vif(X_train)
| feature | VIF | |
|---|---|---|
| 0 | const | 39382055.37444 |
| 1 | no_of_adults | 1.35516 |
| 2 | no_of_children | 2.08903 |
| 3 | no_of_weekend_nights | 1.05611 |
| 4 | no_of_week_nights | 1.09502 |
| 5 | required_car_parking_space | 1.03984 |
| 6 | lead_time | 1.39009 |
| 7 | arrival_year | 1.42775 |
| 8 | arrival_month | 1.27445 |
| 9 | arrival_date | 1.00663 |
| 10 | repeated_guest | 1.78451 |
| 11 | no_of_previous_cancellations | 1.39570 |
| 12 | no_of_previous_bookings_not_canceled | 1.65209 |
| 13 | avg_price_per_room | 1.97058 |
| 14 | no_of_special_requests | 1.24898 |
| 15 | type_of_meal_plan_Meal Plan 2 | 1.26268 |
| 16 | type_of_meal_plan_Meal Plan 3 | 1.02517 |
| 17 | type_of_meal_plan_Not Selected | 1.27972 |
| 18 | room_type_reserved_Room_Type 2 | 1.10695 |
| 19 | room_type_reserved_Room_Type 3 | 1.00331 |
| 20 | room_type_reserved_Room_Type 4 | 1.37363 |
| 21 | room_type_reserved_Room_Type 5 | 1.02824 |
| 22 | room_type_reserved_Room_Type 6 | 2.01512 |
| 23 | room_type_reserved_Room_Type 7 | 1.09661 |
| 24 | market_segment_type_Complementary | 4.44733 |
| 25 | market_segment_type_Corporate | 16.91930 |
| 26 | market_segment_type_Offline | 64.10093 |
| 27 | market_segment_type_Online | 71.17484 |
Dropping market_segment_type_Online variable reduces the VIF in all the other variables that had a high VIF before to less than 5.
X_train1 = X_train.drop("market_segment_type_Online", axis=1)
vif_series2 = pd.Series(
[variance_inflation_factor(X_train1.values, i) for i in range(X_train1.shape[1])],
index=X_train1.columns,
)
print("Series before feature selection: \n\n{}\n".format(vif_series2))
Series before feature selection: const 39302287.19172 no_of_adults 1.33897 no_of_children 2.08804 no_of_weekend_nights 1.05556 no_of_week_nights 1.09490 required_car_parking_space 1.03972 lead_time 1.38568 arrival_year 1.42520 arrival_month 1.27334 arrival_date 1.00661 repeated_guest 1.78130 no_of_previous_cancellations 1.39545 no_of_previous_bookings_not_canceled 1.65185 avg_price_per_room 1.96959 no_of_special_requests 1.24413 type_of_meal_plan_Meal Plan 2 1.26235 type_of_meal_plan_Meal Plan 3 1.02517 type_of_meal_plan_Not Selected 1.27775 room_type_reserved_Room_Type 2 1.10679 room_type_reserved_Room_Type 3 1.00330 room_type_reserved_Room_Type 4 1.36788 room_type_reserved_Room_Type 5 1.02824 room_type_reserved_Room_Type 6 2.01472 room_type_reserved_Room_Type 7 1.09651 market_segment_type_Complementary 1.28752 market_segment_type_Corporate 1.53650 market_segment_type_Offline 1.61371 dtype: float64
The above process can also be done manually by picking one variable at a time that has a high p-value, dropping it, and building a model again. But that might be a little tedious and using a loop will be more efficient.
# initial list of columns
cols = X_train.columns.tolist()
# setting an initial max p-value
max_p_value = 1
while len(cols) > 0:
# defining the train set
x_train_aux = X_train[cols]
# fitting the model
model = sm.Logit(y_train, x_train_aux).fit(disp=False)
# getting the p-values and the maximum p-value
p_values = model.pvalues
max_p_value = max(p_values)
# name of the variable with maximum p-value
feature_with_p_max = p_values.idxmax()
if max_p_value > 0.05:
cols.remove(feature_with_p_max)
else:
break
selected_features = cols
print(selected_features)
['const', 'no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'required_car_parking_space', 'lead_time', 'arrival_year', 'arrival_month', 'repeated_guest', 'no_of_previous_cancellations', 'avg_price_per_room', 'no_of_special_requests', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Corporate', 'market_segment_type_Offline']
X_train2 = X_train1[selected_features]
X_test1 = X_test[selected_features]
logit1 = sm.Logit(y_train, X_train2.astype(float))
lg1 = logit1.fit(disp = False)
print(lg1.summary())
Logit Regression Results
==============================================================================
Dep. Variable: booking_status No. Observations: 25392
Model: Logit Df Residuals: 25371
Method: MLE Df Model: 20
Date: Sun, 18 Jun 2023 Pseudo R-squ.: 0.3286
Time: 05:51:03 Log-Likelihood: -10803.
converged: True LL-Null: -16091.
Covariance Type: nonrobust LLR p-value: 0.000
==================================================================================================
coef std err z P>|z| [0.025 0.975]
--------------------------------------------------------------------------------------------------
const -981.1614 120.776 -8.124 0.000 -1217.877 -744.445
no_of_adults 0.1062 0.037 2.840 0.005 0.033 0.179
no_of_children 0.1661 0.062 2.689 0.007 0.045 0.287
no_of_weekend_nights 0.1164 0.020 5.959 0.000 0.078 0.155
required_car_parking_space -1.6145 0.139 -11.645 0.000 -1.886 -1.343
lead_time 0.0164 0.000 60.648 0.000 0.016 0.017
arrival_year 0.4847 0.060 8.099 0.000 0.367 0.602
arrival_month -0.0405 0.006 -6.272 0.000 -0.053 -0.028
repeated_guest -2.5980 0.513 -5.060 0.000 -3.604 -1.592
no_of_previous_cancellations 0.2170 0.075 2.896 0.004 0.070 0.364
avg_price_per_room 0.0202 0.001 26.056 0.000 0.019 0.022
no_of_special_requests -1.4735 0.030 -48.932 0.000 -1.532 -1.414
type_of_meal_plan_Meal Plan 2 0.2359 0.065 3.615 0.000 0.108 0.364
type_of_meal_plan_Not Selected 0.2949 0.053 5.562 0.000 0.191 0.399
room_type_reserved_Room_Type 2 -0.3544 0.131 -2.696 0.007 -0.612 -0.097
room_type_reserved_Room_Type 4 -0.2818 0.053 -5.294 0.000 -0.386 -0.178
room_type_reserved_Room_Type 5 -0.7255 0.207 -3.498 0.000 -1.132 -0.319
room_type_reserved_Room_Type 6 -0.7188 0.148 -4.846 0.000 -1.010 -0.428
room_type_reserved_Room_Type 7 -0.7488 0.279 -2.686 0.007 -1.295 -0.203
market_segment_type_Corporate -0.7723 0.103 -7.484 0.000 -0.975 -0.570
market_segment_type_Offline -1.7834 0.052 -34.224 0.000 -1.885 -1.681
==================================================================================================
Coefficient Interpretations
print("Training performance:")
model_performance_classification_statsmodels(lg1, X_train2, y_train)
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80466 | 0.63339 | 0.73662 | 0.68111 |
# converting coefficients to odds
odds = np.exp(lg1.params)
# finding the percentage change
perc_change_odds = (np.exp(lg1.params) - 1) * 100
# removing limit from number of columns to display
pd.set_option("display.max_columns", None)
# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train1.columns).T
| const | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | required_car_parking_space | lead_time | arrival_year | arrival_month | arrival_date | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | type_of_meal_plan_Meal Plan 2 | type_of_meal_plan_Meal Plan 3 | type_of_meal_plan_Not Selected | room_type_reserved_Room_Type 2 | room_type_reserved_Room_Type 3 | room_type_reserved_Room_Type 4 | room_type_reserved_Room_Type 5 | room_type_reserved_Room_Type 6 | room_type_reserved_Room_Type 7 | market_segment_type_Complementary | market_segment_type_Corporate | market_segment_type_Offline | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Odds | 0.00000 | 1.11199 | 1.18072 | 1.12346 | NaN | 0.19899 | 1.01657 | 1.62376 | 0.96030 | NaN | 0.07442 | 1.24231 | NaN | 1.02039 | 0.22913 | 1.26607 | NaN | 1.34303 | 0.70158 | NaN | 0.75439 | 0.48406 | 0.48732 | 0.47293 | NaN | 0.46196 | 0.16807 |
| Change_odd% | -100.00000 | 11.19897 | 18.07198 | 12.34624 | NaN | -80.10148 | 1.65718 | 62.37639 | -3.96972 | NaN | -92.55772 | 24.23133 | NaN | 2.03902 | -77.08689 | 26.60722 | NaN | 34.30253 | -29.84163 | NaN | -24.56115 | -51.59395 | -51.26812 | -52.70650 | NaN | -53.80365 | -83.19280 |
Coefficient interpretations
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train2, y_train)
print("Training performance:")
log_reg_model_train_perf = model_performance_classification_statsmodels(lg1, X_train2, y_train)
log_reg_model_train_perf
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80466 | 0.63339 | 0.73662 | 0.68111 |
logit_roc_auc_train = roc_auc_score(y_train, lg1.predict(X_train2))
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train2))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()
Logistic Regression model is giving a good performance on training set.
# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train2))
optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)
0.36414668973028896
# creating confusion matrix
confusion_matrix_statsmodels(
lg1, X_train2, y_train, threshold=optimal_threshold_auc_roc
)
# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
lg1, X_train2, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.79143 | 0.73777 | 0.66537 | 0.69971 |
y_scores = lg1.predict(X_train2)
prec, rec, tre = precision_recall_curve(y_train, y_scores,)
def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
plt.plot(thresholds, precisions[:-1], "b--", label="precision")
plt.plot(thresholds, recalls[:-1], "g--", label="recall")
plt.xlabel("Threshold")
plt.legend(loc="upper left")
plt.ylim([0, 1])
plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()
At the threshold of 0.42, we get balanced recall and precision.
# setting the threshold
optimal_threshold_curve = 0.42
# creating confusion matrix
confusion_matrix_statsmodels(
lg1, X_train2, y_train, threshold=optimal_threshold_curve
)
log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
lg1, X_train2, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.79883 | 0.69580 | 0.69414 | 0.69497 |
Using model with default threshold
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test)
log_reg_model_test_perf = model_performance_classification_statsmodels(lg1, X_test1, y_test)
print("Test performance:")
log_reg_model_test_perf
Test performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80437 | 0.63430 | 0.72650 | 0.67728 |
logit_roc_auc_train = roc_auc_score(y_test, lg1.predict(X_test1))
fpr, tpr, thresholds = roc_curve(y_test, lg1.predict(X_test1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()
Using model with threshold=0.37
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train2, y_train, threshold=optimal_threshold_auc_roc)
# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
lg1, X_test1, y_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc
Test performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.79482 | 0.74333 | 0.66329 | 0.70103 |
Using model with threshold = 0.42
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test, threshold=optimal_threshold_curve)
log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
lg1, X_test1, y_test, threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve
Test performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80290 | 0.70244 | 0.69280 | 0.69759 |
# training performance comparison
models_train_comp_df = pd.concat(
[
log_reg_model_train_perf.T,
log_reg_model_train_perf_threshold_auc_roc.T,
log_reg_model_train_perf_threshold_curve.T,
],
axis=1,
)
models_train_comp_df.columns = [
"Logistic Regression-default Threshold",
"Logistic Regression-0.37 Threshold",
"Logistic Regression-0.42 Threshold",
]
print("Training performance comparison:")
models_train_comp_df
Training performance comparison:
| Logistic Regression-default Threshold | Logistic Regression-0.37 Threshold | Logistic Regression-0.42 Threshold | |
|---|---|---|---|
| Accuracy | 0.80466 | 0.79143 | 0.79883 |
| Recall | 0.63339 | 0.73777 | 0.69580 |
| Precision | 0.73662 | 0.66537 | 0.69414 |
| F1 | 0.68111 | 0.69971 | 0.69497 |
# test performance comparison
models_test_comp_df = pd.concat(
[
log_reg_model_test_perf.T,
log_reg_model_test_perf_threshold_auc_roc.T,
log_reg_model_test_perf_threshold_curve.T,
],
axis=1,
)
models_test_comp_df.columns = [
"Logistic Regression-default Threshold (0.5)",
"Logistic Regression-0.37 Threshold",
"Logistic Regression-0.42 Threshold",
]
print("Test set performance comparison:")
models_test_comp_df
Test set performance comparison:
| Logistic Regression-default Threshold (0.5) | Logistic Regression-0.37 Threshold | Logistic Regression-0.42 Threshold | |
|---|---|---|---|
| Accuracy | 0.80437 | 0.79482 | 0.80290 |
| Recall | 0.63430 | 0.74333 | 0.70244 |
| Precision | 0.72650 | 0.66329 | 0.69280 |
| F1 | 0.67728 | 0.70103 | 0.69759 |
X = data.drop(["booking_status"], axis=1)
Y = data["booking_status"]
# create dummy variables
X = pd.get_dummies(X, drop_first=True)
# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.30, random_state=1)
print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))
Shape of Training set : (25392, 27) Shape of test set : (10883, 27) Percentage of classes in training set: 0 0.67064 1 0.32936 Name: booking_status, dtype: float64 Percentage of classes in test set: 0 0.67638 1 0.32362 Name: booking_status, dtype: float64
We had seen that around 67.06% of observations belongs to class 0 (Not Cancelled) and 32.93% observations belongs to class 1 (Cancelled), and this is preserved in the train and test sets
# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
"""
Function to compute different metrics to check classification model performance
model: classifier
predictors: independent variables
target: dependent variable
"""
# predicting using the independent variables
pred = model.predict(predictors)
acc = accuracy_score(target, pred) # to compute Accuracy
recall = recall_score(target, pred) # to compute Recall
precision = precision_score(target, pred) # to compute Precision
f1 = f1_score(target, pred) # to compute F1-score
# creating a dataframe of metrics
df_perf = pd.DataFrame(
{"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
index=[0],
)
return df_perf
def confusion_matrix_sklearn(model, predictors, target):
"""
To plot the confusion_matrix with percentages
model: classifier
predictors: independent variables
target: dependent variable
"""
y_pred = model.predict(predictors)
cm = confusion_matrix(target, y_pred)
labels = np.asarray(
[
["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
for item in cm.flatten()
]
).reshape(2, 2)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=labels, fmt="")
plt.ylabel("True label")
plt.xlabel("Predicted label")
model = DecisionTreeClassifier(criterion='gini',random_state=1)
model.fit(X_train, y_train)
DecisionTreeClassifier(random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(random_state=1)
confusion_matrix_sklearn(model, X_train, y_train)
decision_tree_perf_train = model_performance_classification_sklearn(
model, X_train, y_train
)
decision_tree_perf_train
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.99421 | 0.98661 | 0.99578 | 0.99117 |
confusion_matrix_sklearn(model, X_test, y_test)
decision_tree_perf_test = model_performance_classification_sklearn(model, X_test, y_test)
decision_tree_perf_test
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.86971 | 0.80835 | 0.79304 | 0.80062 |
Before pruning the tree let's check the important features.
feature_names = list(X_train.columns)
importances = model.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
The lead time and average price per room are the most important features for predicting booking cancellations.
Pre-Pruning
# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1, class_weight="balanced")
# Grid of parameters to choose from
parameters = {
"max_depth": np.arange(2, 7, 2),
"max_leaf_nodes": [50, 75, 150, 250],
"min_samples_split": [10, 30, 50, 70],
}
# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(f1_score)
# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)
# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_
# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)
DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=50,
min_samples_split=10, random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=50,
min_samples_split=10, random_state=1)confusion_matrix_sklearn(estimator, X_train, y_train)
decision_tree_tune_perf_train = model_performance_classification_sklearn(estimator, X_train, y_train)
decision_tree_tune_perf_train
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.83089 | 0.78620 | 0.72404 | 0.75384 |
confusion_matrix_sklearn(estimator, X_test, y_test)
decision_tree_tune_perf_test = model_performance_classification_sklearn(estimator, X_test, y_test)
decision_tree_tune_perf_test
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.83497 | 0.78336 | 0.72758 | 0.75444 |
The model is giving a generalized result now since the recall scores on both the train and test data are coming to be around 0.78 which shows that the model is able to generalize well on unseen data.
plt.figure(figsize=(20, 10))
out = tree.plot_tree(
estimator,
feature_names=feature_names,
filled=True,
fontsize=9,
node_ids=False,
class_names=None,
)
# below code will add arrows to the decision tree split if they are missing
for o in out:
arrow = o.arrow_patch
if arrow is not None:
arrow.set_edgecolor("black")
arrow.set_linewidth(1)
plt.show()
# Text report showing the rules of a decision tree -
print(tree.export_text(estimator, feature_names=feature_names, show_weights=True))
|--- lead_time <= 151.50 | |--- no_of_special_requests <= 0.50 | | |--- market_segment_type_Online <= 0.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_weekend_nights <= 0.50 | | | | | |--- avg_price_per_room <= 179.47 | | | | | | |--- weights: [1734.15, 132.08] class: 0 | | | | | |--- avg_price_per_room > 179.47 | | | | | | |--- weights: [2.98, 25.81] class: 1 | | | | |--- no_of_weekend_nights > 0.50 | | | | | |--- lead_time <= 68.50 | | | | | | |--- weights: [960.27, 223.16] class: 0 | | | | | |--- lead_time > 68.50 | | | | | | |--- weights: [129.73, 160.92] class: 1 | | | |--- lead_time > 90.50 | | | | |--- lead_time <= 117.50 | | | | | |--- avg_price_per_room <= 93.58 | | | | | | |--- weights: [214.72, 227.72] class: 1 | | | | | |--- avg_price_per_room > 93.58 | | | | | | |--- weights: [82.76, 285.41] class: 1 | | | | |--- lead_time > 117.50 | | | | | |--- no_of_week_nights <= 1.50 | | | | | | |--- weights: [87.23, 81.98] class: 0 | | | | | |--- no_of_week_nights > 1.50 | | | | | | |--- weights: [228.14, 48.58] class: 0 | | |--- market_segment_type_Online > 0.50 | | | |--- lead_time <= 13.50 | | | | |--- avg_price_per_room <= 99.44 | | | | | |--- arrival_month <= 1.50 | | | | | | |--- weights: [92.45, 0.00] class: 0 | | | | | |--- arrival_month > 1.50 | | | | | | |--- weights: [363.83, 132.08] class: 0 | | | | |--- avg_price_per_room > 99.44 | | | | | |--- lead_time <= 3.50 | | | | | | |--- weights: [219.94, 85.01] class: 0 | | | | | |--- lead_time > 3.50 | | | | | | |--- weights: [132.71, 280.85] class: 1 | | | |--- lead_time > 13.50 | | | | |--- required_car_parking_space <= 0.50 | | | | | |--- avg_price_per_room <= 71.92 | | | | | | |--- weights: [158.80, 159.40] class: 1 | | | | | |--- avg_price_per_room > 71.92 | | | | | | |--- weights: [850.67, 3543.28] class: 1 | | | | |--- required_car_parking_space > 0.50 | | | | | |--- no_of_weekend_nights <= 3.00 | | | | | | |--- weights: [48.46, 0.00] class: 0 | | | | | |--- no_of_weekend_nights > 3.00 | | | | | | |--- weights: [0.00, 1.52] class: 1 | |--- no_of_special_requests > 0.50 | | |--- no_of_special_requests <= 1.50 | | | |--- market_segment_type_Online <= 0.50 | | | | |--- lead_time <= 102.50 | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | |--- weights: [697.09, 9.11] class: 0 | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | |--- weights: [15.66, 9.11] class: 0 | | | | |--- lead_time > 102.50 | | | | | |--- no_of_week_nights <= 2.50 | | | | | | |--- weights: [32.06, 19.74] class: 0 | | | | | |--- no_of_week_nights > 2.50 | | | | | | |--- weights: [44.73, 3.04] class: 0 | | | |--- market_segment_type_Online > 0.50 | | | | |--- lead_time <= 8.50 | | | | | |--- lead_time <= 4.50 | | | | | | |--- weights: [498.03, 44.03] class: 0 | | | | | |--- lead_time > 4.50 | | | | | | |--- weights: [258.71, 63.76] class: 0 | | | | |--- lead_time > 8.50 | | | | | |--- required_car_parking_space <= 0.50 | | | | | | |--- weights: [2512.51, 1451.32] class: 0 | | | | | |--- required_car_parking_space > 0.50 | | | | | | |--- weights: [134.20, 1.52] class: 0 | | |--- no_of_special_requests > 1.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_week_nights <= 3.50 | | | | | |--- weights: [1585.04, 0.00] class: 0 | | | | |--- no_of_week_nights > 3.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- weights: [180.42, 57.69] class: 0 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [52.19, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- no_of_special_requests <= 2.50 | | | | | |--- arrival_month <= 8.50 | | | | | | |--- weights: [184.90, 56.17] class: 0 | | | | | |--- arrival_month > 8.50 | | | | | | |--- weights: [106.61, 106.27] class: 0 | | | | |--- no_of_special_requests > 2.50 | | | | | |--- weights: [67.10, 0.00] class: 0 |--- lead_time > 151.50 | |--- avg_price_per_room <= 100.04 | | |--- no_of_special_requests <= 0.50 | | | |--- no_of_adults <= 1.50 | | | | |--- market_segment_type_Online <= 0.50 | | | | | |--- lead_time <= 163.50 | | | | | | |--- weights: [3.73, 24.29] class: 1 | | | | | |--- lead_time > 163.50 | | | | | | |--- weights: [257.96, 62.24] class: 0 | | | | |--- market_segment_type_Online > 0.50 | | | | | |--- avg_price_per_room <= 35.22 | | | | | | |--- weights: [8.95, 4.55] class: 0 | | | | | |--- avg_price_per_room > 35.22 | | | | | | |--- weights: [0.75, 95.64] class: 1 | | | |--- no_of_adults > 1.50 | | | | |--- avg_price_per_room <= 82.47 | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | |--- weights: [2.98, 282.37] class: 1 | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | |--- weights: [213.97, 385.60] class: 1 | | | | |--- avg_price_per_room > 82.47 | | | | | |--- no_of_adults <= 2.50 | | | | | | |--- weights: [23.86, 1030.80] class: 1 | | | | | |--- no_of_adults > 2.50 | | | | | | |--- weights: [5.22, 0.00] class: 0 | | |--- no_of_special_requests > 0.50 | | | |--- no_of_weekend_nights <= 0.50 | | | | |--- lead_time <= 180.50 | | | | | |--- lead_time <= 159.50 | | | | | | |--- weights: [7.46, 7.59] class: 1 | | | | | |--- lead_time > 159.50 | | | | | | |--- weights: [37.28, 4.55] class: 0 | | | | |--- lead_time > 180.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- weights: [20.13, 212.54] class: 1 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [8.95, 0.00] class: 0 | | | |--- no_of_weekend_nights > 0.50 | | | | |--- market_segment_type_Offline <= 0.50 | | | | | |--- arrival_month <= 11.50 | | | | | | |--- weights: [231.12, 110.82] class: 0 | | | | | |--- arrival_month > 11.50 | | | | | | |--- weights: [19.38, 34.92] class: 1 | | | | |--- market_segment_type_Offline > 0.50 | | | | | |--- weights: [112.58, 7.59] class: 0 | |--- avg_price_per_room > 100.04 | | |--- arrival_month <= 11.50 | | | |--- no_of_special_requests <= 2.50 | | | | |--- weights: [0.00, 3200.19] class: 1 | | | |--- no_of_special_requests > 2.50 | | | | |--- weights: [23.11, 0.00] class: 0 | | |--- arrival_month > 11.50 | | | |--- no_of_special_requests <= 0.50 | | | | |--- weights: [35.04, 0.00] class: 0 | | | |--- no_of_special_requests > 0.50 | | | | |--- arrival_date <= 24.50 | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | |--- arrival_date > 24.50 | | | | | |--- weights: [3.73, 22.77] class: 1
Observations from the pre-pruned tree:
Using the above extracted decision rules we can make interpretations from the decision tree model like:
# importance of features in the tree building
importances = estimator.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
Cost Complexity Pruning
clf = DecisionTreeClassifier(random_state=1, class_weight="balanced")
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = abs(path.ccp_alphas), path.impurities
pd.DataFrame(path)
| ccp_alphas | impurities | |
|---|---|---|
| 0 | 0.00000 | 0.00838 |
| 1 | 0.00000 | 0.00838 |
| 2 | 0.00000 | 0.00838 |
| 3 | 0.00000 | 0.00838 |
| 4 | 0.00000 | 0.00838 |
| ... | ... | ... |
| 1861 | 0.00890 | 0.32806 |
| 1862 | 0.00980 | 0.33786 |
| 1863 | 0.01272 | 0.35058 |
| 1864 | 0.03412 | 0.41882 |
| 1865 | 0.08118 | 0.50000 |
1866 rows × 2 columns
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()
Next, we train a decision tree using effective alphas. The last value
in ccp_alphas is the alpha value that prunes the whole tree,
leaving the tree, clfs[-1], with one node.
clfs = []
for ccp_alpha in ccp_alphas:
clf = DecisionTreeClassifier(
random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
)
clf.fit(X_train, y_train)
clfs.append(clf)
print(
"Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
clfs[-1].tree_.node_count, ccp_alphas[-1]
)
)
Number of nodes in the last tree is: 1 with ccp_alpha: 0.08117914389136938
For the remainder, we remove the last element in
clfs and ccp_alphas, because it is the trivial tree with only one
node. Here we show that the number of nodes and tree depth decreases as alpha
increases.
clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]
node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()
f1_train = []
for clf in clfs:
pred_train = clf.predict(X_train)
values_train = f1_score(y_train, pred_train)
f1_train.append(values_train)
f1_test = []
for clf in clfs:
pred_test = clf.predict(X_test)
values_test = f1_score(y_test, pred_test)
f1_test.append(values_test)
fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("F1 Score")
ax.set_title("F1 Score vs alpha for training and testing sets")
ax.plot(ccp_alphas, f1_train, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, f1_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()
index_best_model = np.argmax(f1_test)
best_model = clfs[index_best_model]
print(best_model)
DecisionTreeClassifier(ccp_alpha=0.00012291224171537198,
class_weight='balanced', random_state=1)
confusion_matrix_sklearn(best_model, X_train, y_train)
decision_tree_post_perf_train = model_performance_classification_sklearn(
best_model, X_train, y_train
)
decision_tree_post_perf_train
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.89721 | 0.90207 | 0.80814 | 0.85253 |
confusion_matrix_sklearn(best_model, X_test, y_test)
decision_tree_post_test = model_performance_classification_sklearn(
best_model, X_test, y_test)
decision_tree_post_test
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.86778 | 0.85576 | 0.76401 | 0.80729 |
plt.figure(figsize=(20, 10))
out = tree.plot_tree(
best_model,
feature_names=feature_names,
filled=True,
fontsize=9,
node_ids=False,
class_names=None,
)
for o in out:
arrow = o.arrow_patch
if arrow is not None:
arrow.set_edgecolor("black")
arrow.set_linewidth(1)
plt.show()
# Text report showing the rules of a decision tree -
print(tree.export_text(best_model, feature_names=feature_names, show_weights=True))
|--- lead_time <= 151.50 | |--- no_of_special_requests <= 0.50 | | |--- market_segment_type_Online <= 0.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_weekend_nights <= 0.50 | | | | | |--- avg_price_per_room <= 179.47 | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | |--- lead_time <= 16.50 | | | | | | | | |--- avg_price_per_room <= 68.50 | | | | | | | | | |--- weights: [207.26, 10.63] class: 0 | | | | | | | | |--- avg_price_per_room > 68.50 | | | | | | | | | |--- arrival_date <= 29.50 | | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | |--- arrival_date > 29.50 | | | | | | | | | | |--- weights: [2.24, 7.59] class: 1 | | | | | | | |--- lead_time > 16.50 | | | | | | | | |--- avg_price_per_room <= 135.00 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- no_of_previous_bookings_not_canceled <= 0.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- no_of_previous_bookings_not_canceled > 0.50 | | | | | | | | | | | |--- weights: [11.18, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- weights: [21.62, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 135.00 | | | | | | | | | |--- weights: [0.00, 12.14] class: 1 | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | |--- weights: [1197.36, 0.00] class: 0 | | | | | |--- avg_price_per_room > 179.47 | | | | | | |--- weights: [2.98, 25.81] class: 1 | | | | |--- no_of_weekend_nights > 0.50 | | | | | |--- lead_time <= 68.50 | | | | | | |--- arrival_month <= 9.50 | | | | | | | |--- avg_price_per_room <= 63.29 | | | | | | | | |--- arrival_date <= 20.50 | | | | | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | | | | | |--- weights: [41.75, 0.00] class: 0 | | | | | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | | | | | |--- weights: [0.75, 3.04] class: 1 | | | | | | | | |--- arrival_date > 20.50 | | | | | | | | | |--- avg_price_per_room <= 59.75 | | | | | | | | | | |--- arrival_date <= 23.50 | | | | | | | | | | | |--- weights: [1.49, 12.14] class: 1 | | | | | | | | | | |--- arrival_date > 23.50 | | | | | | | | | | | |--- weights: [14.91, 1.52] class: 0 | | | | | | | | | |--- avg_price_per_room > 59.75 | | | | | | | | | | |--- lead_time <= 44.00 | | | | | | | | | | | |--- weights: [0.75, 59.21] class: 1 | | | | | | | | | | |--- lead_time > 44.00 | | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | |--- avg_price_per_room > 63.29 | | | | | | | | |--- no_of_weekend_nights <= 3.50 | | | | | | | | | |--- lead_time <= 59.50 | | | | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_month > 7.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- lead_time > 59.50 | | | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | | | |--- weights: [20.13, 0.00] class: 0 | | | | | | | | |--- no_of_weekend_nights > 3.50 | | | | | | | | | |--- weights: [0.75, 15.18] class: 1 | | | | | | |--- arrival_month > 9.50 | | | | | | | |--- weights: [413.04, 27.33] class: 0 | | | | | |--- lead_time > 68.50 | | | | | | |--- avg_price_per_room <= 99.98 | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | |--- avg_price_per_room <= 62.50 | | | | | | | | | |--- weights: [15.66, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 62.50 | | | | | | | | | |--- avg_price_per_room <= 80.38 | | | | | | | | | | |--- weights: [8.20, 25.81] class: 1 | | | | | | | | | |--- avg_price_per_room > 80.38 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | |--- arrival_month > 3.50 | | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | | |--- weights: [55.17, 3.04] class: 0 | | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | | |--- lead_time <= 73.50 | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | | |--- lead_time > 73.50 | | | | | | | | | | |--- weights: [21.62, 4.55] class: 0 | | | | | | |--- avg_price_per_room > 99.98 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- weights: [8.95, 0.00] class: 0 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- avg_price_per_room <= 132.43 | | | | | | | | | |--- weights: [9.69, 122.97] class: 1 | | | | | | | | |--- avg_price_per_room > 132.43 | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- lead_time <= 117.50 | | | | | |--- avg_price_per_room <= 93.58 | | | | | | |--- avg_price_per_room <= 75.07 | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | |--- avg_price_per_room <= 58.75 | | | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 58.75 | | | | | | | | | |--- repeated_guest <= 0.50 | | | | | | | | | | |--- arrival_month <= 4.50 | | | | | | | | | | | |--- weights: [2.24, 118.41] class: 1 | | | | | | | | | | |--- arrival_month > 4.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- repeated_guest > 0.50 | | | | | | | | | | |--- weights: [4.47, 0.00] class: 0 | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | |--- arrival_date <= 11.50 | | | | | | | | | |--- weights: [31.31, 0.00] class: 0 | | | | | | | | |--- arrival_date > 11.50 | | | | | | | | | |--- weights: [29.08, 15.18] class: 0 | | | | | | |--- avg_price_per_room > 75.07 | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | |--- weights: [59.64, 3.04] class: 0 | | | | | | | |--- arrival_month > 3.50 | | | | | | | | |--- arrival_month <= 4.50 | | | | | | | | | |--- weights: [1.49, 16.70] class: 1 | | | | | | | | |--- arrival_month > 4.50 | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | |--- avg_price_per_room <= 86.00 | | | | | | | | | | | |--- weights: [2.24, 16.70] class: 1 | | | | | | | | | | |--- avg_price_per_room > 86.00 | | | | | | | | | | | |--- weights: [8.95, 3.04] class: 0 | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | |--- arrival_date <= 22.50 | | | | | | | | | | | |--- weights: [44.73, 4.55] class: 0 | | | | | | | | | | |--- arrival_date > 22.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | |--- avg_price_per_room > 93.58 | | | | | | |--- arrival_date <= 11.50 | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | |--- weights: [16.40, 39.47] class: 1 | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | |--- weights: [20.13, 6.07] class: 0 | | | | | | |--- arrival_date > 11.50 | | | | | | | |--- avg_price_per_room <= 102.09 | | | | | | | | |--- weights: [5.22, 144.22] class: 1 | | | | | | | |--- avg_price_per_room > 102.09 | | | | | | | | |--- avg_price_per_room <= 109.50 | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | |--- weights: [0.75, 16.70] class: 1 | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | |--- weights: [33.55, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 109.50 | | | | | | | | | |--- avg_price_per_room <= 124.25 | | | | | | | | | | |--- weights: [2.98, 75.91] class: 1 | | | | | | | | | |--- avg_price_per_room > 124.25 | | | | | | | | | | |--- weights: [3.73, 3.04] class: 0 | | | | |--- lead_time > 117.50 | | | | | |--- no_of_week_nights <= 1.50 | | | | | | |--- arrival_date <= 7.50 | | | | | | | |--- weights: [38.02, 0.00] class: 0 | | | | | | |--- arrival_date > 7.50 | | | | | | | |--- avg_price_per_room <= 93.58 | | | | | | | | |--- avg_price_per_room <= 65.38 | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | |--- avg_price_per_room > 65.38 | | | | | | | | | |--- weights: [24.60, 3.04] class: 0 | | | | | | | |--- avg_price_per_room > 93.58 | | | | | | | | |--- arrival_date <= 28.00 | | | | | | | | | |--- weights: [14.91, 72.87] class: 1 | | | | | | | | |--- arrival_date > 28.00 | | | | | | | | | |--- weights: [9.69, 1.52] class: 0 | | | | | |--- no_of_week_nights > 1.50 | | | | | | |--- no_of_adults <= 1.50 | | | | | | | |--- weights: [84.25, 0.00] class: 0 | | | | | | |--- no_of_adults > 1.50 | | | | | | | |--- lead_time <= 125.50 | | | | | | | | |--- avg_price_per_room <= 90.85 | | | | | | | | | |--- avg_price_per_room <= 87.50 | | | | | | | | | | |--- weights: [13.42, 13.66] class: 1 | | | | | | | | | |--- avg_price_per_room > 87.50 | | | | | | | | | | |--- weights: [0.00, 15.18] class: 1 | | | | | | | | |--- avg_price_per_room > 90.85 | | | | | | | | | |--- weights: [10.44, 0.00] class: 0 | | | | | | | |--- lead_time > 125.50 | | | | | | | | |--- arrival_date <= 19.50 | | | | | | | | | |--- weights: [58.15, 18.22] class: 0 | | | | | | | | |--- arrival_date > 19.50 | | | | | | | | | |--- weights: [61.88, 1.52] class: 0 | | |--- market_segment_type_Online > 0.50 | | | |--- lead_time <= 13.50 | | | | |--- avg_price_per_room <= 99.44 | | | | | |--- arrival_month <= 1.50 | | | | | | |--- weights: [92.45, 0.00] class: 0 | | | | | |--- arrival_month > 1.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | |--- avg_price_per_room <= 70.05 | | | | | | | | | |--- weights: [31.31, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 70.05 | | | | | | | | | |--- lead_time <= 5.50 | | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | | |--- weights: [38.77, 1.52] class: 0 | | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | |--- lead_time > 5.50 | | | | | | | | | | |--- arrival_date <= 3.50 | | | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 3.50 | | | | | | | | | | | |--- weights: [34.30, 40.99] class: 1 | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | |--- weights: [0.00, 19.74] class: 1 | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | |--- lead_time <= 2.50 | | | | | | | | | | |--- avg_price_per_room <= 74.21 | | | | | | | | | | | |--- weights: [0.75, 3.04] class: 1 | | | | | | | | | | |--- avg_price_per_room > 74.21 | | | | | | | | | | | |--- weights: [9.69, 0.00] class: 0 | | | | | | | | | |--- lead_time > 2.50 | | | | | | | | | | |--- weights: [4.47, 10.63] class: 1 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | |--- weights: [155.07, 6.07] class: 0 | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- weights: [3.73, 10.63] class: 1 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [7.46, 0.00] class: 0 | | | | |--- avg_price_per_room > 99.44 | | | | | |--- lead_time <= 3.50 | | | | | | |--- avg_price_per_room <= 178.78 | | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | |--- weights: [58.15, 30.36] class: 0 | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | |--- weights: [145.38, 22.77] class: 0 | | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | | |--- weights: [0.00, 6.07] class: 1 | | | | | | |--- avg_price_per_room > 178.78 | | | | | | | |--- weights: [16.40, 25.81] class: 1 | | | | | |--- lead_time > 3.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- avg_price_per_room <= 119.25 | | | | | | | | |--- avg_price_per_room <= 118.50 | | | | | | | | | |--- weights: [18.64, 59.21] class: 1 | | | | | | | | |--- avg_price_per_room > 118.50 | | | | | | | | | |--- weights: [8.20, 1.52] class: 0 | | | | | | | |--- avg_price_per_room > 119.25 | | | | | | | | |--- weights: [34.30, 171.55] class: 1 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- weights: [26.09, 1.52] class: 0 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- arrival_date <= 14.00 | | | | | | | | | | |--- weights: [9.69, 36.43] class: 1 | | | | | | | | | |--- arrival_date > 14.00 | | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | | |--- weights: [11.18, 0.00] class: 0 | | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | | |--- weights: [8.95, 10.63] class: 1 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [15.66, 0.00] class: 0 | | | |--- lead_time > 13.50 | | | | |--- required_car_parking_space <= 0.50 | | | | | |--- avg_price_per_room <= 71.92 | | | | | | |--- avg_price_per_room <= 59.43 | | | | | | | |--- lead_time <= 84.50 | | | | | | | | |--- weights: [50.70, 7.59] class: 0 | | | | | | | |--- lead_time > 84.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- arrival_date <= 27.00 | | | | | | | | | | |--- lead_time <= 131.50 | | | | | | | | | | | |--- weights: [0.75, 15.18] class: 1 | | | | | | | | | | |--- lead_time > 131.50 | | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | | | |--- arrival_date > 27.00 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- weights: [10.44, 0.00] class: 0 | | | | | | |--- avg_price_per_room > 59.43 | | | | | | | |--- lead_time <= 25.50 | | | | | | | | |--- weights: [20.88, 6.07] class: 0 | | | | | | | |--- lead_time > 25.50 | | | | | | | | |--- avg_price_per_room <= 71.34 | | | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | | | |--- lead_time <= 68.50 | | | | | | | | | | | |--- weights: [15.66, 78.94] class: 1 | | | | | | | | | | |--- lead_time > 68.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- arrival_month > 3.50 | | | | | | | | | | |--- lead_time <= 102.00 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- lead_time > 102.00 | | | | | | | | | | | |--- weights: [12.67, 3.04] class: 0 | | | | | | | | |--- avg_price_per_room > 71.34 | | | | | | | | | |--- weights: [11.18, 0.00] class: 0 | | | | | |--- avg_price_per_room > 71.92 | | | | | | |--- arrival_year <= 2017.50 | | | | | | | |--- lead_time <= 65.50 | | | | | | | | |--- avg_price_per_room <= 120.45 | | | | | | | | | |--- weights: [79.77, 9.11] class: 0 | | | | | | | | |--- avg_price_per_room > 120.45 | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | |--- weights: [3.73, 12.14] class: 1 | | | | | | | |--- lead_time > 65.50 | | | | | | | | |--- type_of_meal_plan_Meal Plan 2 <= 0.50 | | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | | |--- weights: [16.40, 47.06] class: 1 | | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | |--- type_of_meal_plan_Meal Plan 2 > 0.50 | | | | | | | | | |--- weights: [0.00, 63.76] class: 1 | | | | | | |--- arrival_year > 2017.50 | | | | | | | |--- avg_price_per_room <= 104.31 | | | | | | | | |--- lead_time <= 25.50 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | | |--- weights: [16.40, 0.00] class: 0 | | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | | |--- weights: [38.77, 118.41] class: 1 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- weights: [23.11, 0.00] class: 0 | | | | | | | | |--- lead_time > 25.50 | | | | | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | | |--- weights: [39.51, 185.21] class: 1 | | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | | | | | |--- weights: [73.81, 411.41] class: 1 | | | | | | | |--- avg_price_per_room > 104.31 | | | | | | | | |--- arrival_month <= 10.50 | | | | | | | | | |--- room_type_reserved_Room_Type 5 <= 0.50 | | | | | | | | | | |--- avg_price_per_room <= 144.76 | | | | | | | | | | | |--- truncated branch of depth 10 | | | | | | | | | | |--- avg_price_per_room > 144.76 | | | | | | | | | | | |--- weights: [71.57, 669.49] class: 1 | | | | | | | | | |--- room_type_reserved_Room_Type 5 > 0.50 | | | | | | | | | | |--- arrival_date <= 22.50 | | | | | | | | | | | |--- weights: [11.18, 6.07] class: 0 | | | | | | | | | | |--- arrival_date > 22.50 | | | | | | | | | | | |--- weights: [0.75, 9.11] class: 1 | | | | | | | | |--- arrival_month > 10.50 | | | | | | | | | |--- avg_price_per_room <= 168.06 | | | | | | | | | | |--- lead_time <= 22.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 22.00 | | | | | | | | | | | |--- weights: [17.15, 83.50] class: 1 | | | | | | | | | |--- avg_price_per_room > 168.06 | | | | | | | | | | |--- weights: [12.67, 6.07] class: 0 | | | | |--- required_car_parking_space > 0.50 | | | | | |--- weights: [48.46, 1.52] class: 0 | |--- no_of_special_requests > 0.50 | | |--- no_of_special_requests <= 1.50 | | | |--- market_segment_type_Online <= 0.50 | | | | |--- lead_time <= 102.50 | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | |--- weights: [697.09, 9.11] class: 0 | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | |--- lead_time <= 63.00 | | | | | | | |--- weights: [15.66, 1.52] class: 0 | | | | | | |--- lead_time > 63.00 | | | | | | | |--- weights: [0.00, 7.59] class: 1 | | | | |--- lead_time > 102.50 | | | | | |--- no_of_week_nights <= 2.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- weights: [31.31, 13.66] class: 0 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- weights: [0.75, 6.07] class: 1 | | | | | |--- no_of_week_nights > 2.50 | | | | | | |--- weights: [44.73, 3.04] class: 0 | | | |--- market_segment_type_Online > 0.50 | | | | |--- lead_time <= 8.50 | | | | | |--- lead_time <= 4.50 | | | | | | |--- no_of_weekend_nights <= 3.50 | | | | | | | |--- weights: [497.28, 40.99] class: 0 | | | | | | |--- no_of_weekend_nights > 3.50 | | | | | | | |--- weights: [0.75, 3.04] class: 1 | | | | | |--- lead_time > 4.50 | | | | | | |--- arrival_date <= 13.50 | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | |--- weights: [58.90, 36.43] class: 0 | | | | | | | |--- arrival_month > 9.50 | | | | | | | | |--- weights: [33.55, 1.52] class: 0 | | | | | | |--- arrival_date > 13.50 | | | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | | | |--- weights: [123.76, 9.11] class: 0 | | | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | | | |--- avg_price_per_room <= 126.33 | | | | | | | | | |--- weights: [32.80, 3.04] class: 0 | | | | | | | | |--- avg_price_per_room > 126.33 | | | | | | | | | |--- weights: [9.69, 13.66] class: 1 | | | | |--- lead_time > 8.50 | | | | | |--- required_car_parking_space <= 0.50 | | | | | | |--- avg_price_per_room <= 118.55 | | | | | | | |--- lead_time <= 61.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | |--- weights: [70.08, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | | | | | |--- truncated branch of depth 11 | | | | | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [126.74, 1.52] class: 0 | | | | | | | |--- lead_time > 61.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | | | |--- weights: [4.47, 57.69] class: 1 | | | | | | | | | |--- arrival_month > 7.50 | | | | | | | | | | |--- lead_time <= 66.50 | | | | | | | | | | | |--- weights: [5.22, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 66.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | |--- avg_price_per_room <= 71.93 | | | | | | | | | | | |--- weights: [54.43, 3.04] class: 0 | | | | | | | | | | |--- avg_price_per_room > 71.93 | | | | | | | | | | | |--- truncated branch of depth 10 | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | |--- avg_price_per_room > 118.55 | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | |--- arrival_date <= 19.50 | | | | | | | | | |--- avg_price_per_room <= 177.15 | | | | | | | | | | |--- avg_price_per_room <= 118.98 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- avg_price_per_room > 118.98 | | | | | | | | | | | |--- truncated branch of depth 7 | | | | | | | | | |--- avg_price_per_room > 177.15 | | | | | | | | | | |--- arrival_date <= 7.00 | | | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 7.00 | | | | | | | | | | | |--- weights: [12.67, 24.29] class: 1 | | | | | | | | |--- arrival_date > 19.50 | | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | | |--- avg_price_per_room <= 121.20 | | | | | | | | | | | |--- weights: [18.64, 6.07] class: 0 | | | | | | | | | | |--- avg_price_per_room > 121.20 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | | |--- lead_time <= 55.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 55.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | |--- arrival_month > 8.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | |--- weights: [11.93, 10.63] class: 0 | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | |--- weights: [37.28, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- avg_price_per_room <= 119.20 | | | | | | | | | | | |--- weights: [9.69, 28.84] class: 1 | | | | | | | | | | |--- avg_price_per_room > 119.20 | | | | | | | | | | | |--- truncated branch of depth 12 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- lead_time <= 100.00 | | | | | | | | | | | |--- weights: [49.95, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 100.00 | | | | | | | | | | | |--- weights: [0.75, 18.22] class: 1 | | | | | |--- required_car_parking_space > 0.50 | | | | | | |--- weights: [134.20, 1.52] class: 0 | | |--- no_of_special_requests > 1.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_week_nights <= 3.50 | | | | | |--- weights: [1585.04, 0.00] class: 0 | | | | |--- no_of_week_nights > 3.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- lead_time <= 6.50 | | | | | | | |--- weights: [32.06, 1.52] class: 0 | | | | | | |--- lead_time > 6.50 | | | | | | | |--- room_type_reserved_Room_Type 4 <= 0.50 | | | | | | | | |--- weights: [103.63, 50.10] class: 0 | | | | | | | |--- room_type_reserved_Room_Type 4 > 0.50 | | | | | | | | |--- weights: [44.73, 6.07] class: 0 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [52.19, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- no_of_special_requests <= 2.50 | | | | | |--- arrival_month <= 8.50 | | | | | | |--- lead_time <= 150.50 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- weights: [8.20, 12.14] class: 1 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- no_of_children <= 0.50 | | | | | | | | | |--- avg_price_per_room <= 157.50 | | | | | | | | | | |--- weights: [140.91, 13.66] class: 0 | | | | | | | | | |--- avg_price_per_room > 157.50 | | | | | | | | | | |--- arrival_date <= 12.50 | | | | | | | | | | | |--- weights: [1.49, 6.07] class: 1 | | | | | | | | | | |--- arrival_date > 12.50 | | | | | | | | | | | |--- weights: [5.22, 0.00] class: 0 | | | | | | | | |--- no_of_children > 0.50 | | | | | | | | | |--- weights: [27.59, 16.70] class: 0 | | | | | | |--- lead_time > 150.50 | | | | | | | |--- weights: [1.49, 7.59] class: 1 | | | | | |--- arrival_month > 8.50 | | | | | | |--- avg_price_per_room <= 153.15 | | | | | | | |--- room_type_reserved_Room_Type 2 <= 0.50 | | | | | | | | |--- avg_price_per_room <= 71.12 | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 71.12 | | | | | | | | | |--- avg_price_per_room <= 90.42 | | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | | |--- weights: [12.67, 7.59] class: 0 | | | | | | | | | |--- avg_price_per_room > 90.42 | | | | | | | | | | |--- weights: [64.12, 60.72] class: 0 | | | | | | | |--- room_type_reserved_Room_Type 2 > 0.50 | | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | |--- avg_price_per_room > 153.15 | | | | | | | |--- weights: [12.67, 3.04] class: 0 | | | | |--- no_of_special_requests > 2.50 | | | | | |--- weights: [67.10, 0.00] class: 0 |--- lead_time > 151.50 | |--- avg_price_per_room <= 100.04 | | |--- no_of_special_requests <= 0.50 | | | |--- no_of_adults <= 1.50 | | | | |--- market_segment_type_Online <= 0.50 | | | | | |--- lead_time <= 163.50 | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | |--- weights: [2.98, 0.00] class: 0 | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | |--- weights: [0.75, 24.29] class: 1 | | | | | |--- lead_time > 163.50 | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | |--- lead_time <= 173.00 | | | | | | | | |--- arrival_date <= 3.50 | | | | | | | | | |--- weights: [46.97, 9.11] class: 0 | | | | | | | | |--- arrival_date > 3.50 | | | | | | | | | |--- no_of_weekend_nights <= 1.00 | | | | | | | | | | |--- weights: [0.00, 13.66] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 1.00 | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | |--- lead_time > 173.00 | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | |--- avg_price_per_room <= 88.00 | | | | | | | | | | |--- avg_price_per_room <= 65.00 | | | | | | | | | | | |--- weights: [0.00, 3.04] class: 1 | | | | | | | | | | |--- avg_price_per_room > 65.00 | | | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 88.00 | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | |--- lead_time <= 278.00 | | | | | | | | | | |--- weights: [143.15, 4.55] class: 0 | | | | | | | | | |--- lead_time > 278.00 | | | | | | | | | | |--- avg_price_per_room <= 83.50 | | | | | | | | | | | |--- weights: [20.13, 0.00] class: 0 | | | | | | | | | | |--- avg_price_per_room > 83.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | |--- weights: [2.24, 12.14] class: 1 | | | | |--- market_segment_type_Online > 0.50 | | | | | |--- avg_price_per_room <= 35.22 | | | | | | |--- lead_time <= 285.50 | | | | | | | |--- weights: [8.20, 0.00] class: 0 | | | | | | |--- lead_time > 285.50 | | | | | | | |--- weights: [0.75, 4.55] class: 1 | | | | | |--- avg_price_per_room > 35.22 | | | | | | |--- weights: [0.75, 95.64] class: 1 | | | |--- no_of_adults > 1.50 | | | | |--- avg_price_per_room <= 82.47 | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | |--- weights: [2.98, 282.37] class: 1 | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | |--- arrival_month <= 11.50 | | | | | | | |--- lead_time <= 244.00 | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | | |--- lead_time <= 166.50 | | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 166.50 | | | | | | | | | | | |--- weights: [2.24, 57.69] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | | |--- weights: [17.89, 0.00] class: 0 | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | | |--- weights: [11.18, 3.04] class: 0 | | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | | |--- weights: [0.00, 12.14] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | |--- weights: [75.30, 12.14] class: 0 | | | | | | | |--- lead_time > 244.00 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- weights: [25.35, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- avg_price_per_room <= 80.38 | | | | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | | | | |--- weights: [11.18, 264.15] class: 1 | | | | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- avg_price_per_room > 80.38 | | | | | | | | | | |--- weights: [7.46, 0.00] class: 0 | | | | | | |--- arrival_month > 11.50 | | | | | | | |--- weights: [46.22, 0.00] class: 0 | | | | |--- avg_price_per_room > 82.47 | | | | | |--- no_of_adults <= 2.50 | | | | | | |--- type_of_meal_plan_Meal Plan 2 <= 0.50 | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | |--- room_type_reserved_Room_Type 4 <= 0.50 | | | | | | | | | |--- weights: [8.95, 982.22] class: 1 | | | | | | | | |--- room_type_reserved_Room_Type 4 > 0.50 | | | | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | | | | |--- weights: [0.00, 10.63] class: 1 | | | | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | | | | |--- weights: [4.47, 0.00] class: 0 | | | | | | | |--- arrival_month > 11.50 | | | | | | | | |--- market_segment_type_Online <= 0.50 | | | | | | | | | |--- weights: [5.22, 0.00] class: 0 | | | | | | | | |--- market_segment_type_Online > 0.50 | | | | | | | | | |--- weights: [0.00, 21.25] class: 1 | | | | | | |--- type_of_meal_plan_Meal Plan 2 > 0.50 | | | | | | | |--- avg_price_per_room <= 87.00 | | | | | | | | |--- weights: [0.00, 16.70] class: 1 | | | | | | | |--- avg_price_per_room > 87.00 | | | | | | | | |--- weights: [5.22, 0.00] class: 0 | | | | | |--- no_of_adults > 2.50 | | | | | | |--- weights: [5.22, 0.00] class: 0 | | |--- no_of_special_requests > 0.50 | | | |--- no_of_weekend_nights <= 0.50 | | | | |--- lead_time <= 180.50 | | | | | |--- lead_time <= 159.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- weights: [1.49, 7.59] class: 1 | | | | | |--- lead_time > 159.50 | | | | | | |--- arrival_date <= 1.50 | | | | | | | |--- weights: [1.49, 3.04] class: 1 | | | | | | |--- arrival_date > 1.50 | | | | | | | |--- weights: [35.79, 1.52] class: 0 | | | | |--- lead_time > 180.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- market_segment_type_Online <= 0.50 | | | | | | | |--- avg_price_per_room <= 96.37 | | | | | | | | |--- weights: [12.67, 3.04] class: 0 | | | | | | | |--- avg_price_per_room > 96.37 | | | | | | | | |--- weights: [0.00, 3.04] class: 1 | | | | | | |--- market_segment_type_Online > 0.50 | | | | | | | |--- weights: [7.46, 206.46] class: 1 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [8.95, 0.00] class: 0 | | | |--- no_of_weekend_nights > 0.50 | | | | |--- market_segment_type_Offline <= 0.50 | | | | | |--- arrival_month <= 11.50 | | | | | | |--- avg_price_per_room <= 76.48 | | | | | | | |--- weights: [46.97, 4.55] class: 0 | | | | | | |--- avg_price_per_room > 76.48 | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | |--- no_of_week_nights <= 5.50 | | | | | | | | | |--- lead_time <= 233.00 | | | | | | | | | | |--- lead_time <= 152.50 | | | | | | | | | | | |--- weights: [1.49, 4.55] class: 1 | | | | | | | | | | |--- lead_time > 152.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- lead_time > 233.00 | | | | | | | | | | |--- weights: [23.11, 19.74] class: 0 | | | | | | | | |--- no_of_week_nights > 5.50 | | | | | | | | | |--- weights: [8.95, 16.70] class: 1 | | | | | | | |--- arrival_date > 27.50 | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | |--- weights: [2.24, 15.18] class: 1 | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | |--- lead_time <= 269.00 | | | | | | | | | | |--- lead_time <= 176.00 | | | | | | | | | | | |--- weights: [2.24, 7.59] class: 1 | | | | | | | | | | |--- lead_time > 176.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | |--- lead_time > 269.00 | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | |--- arrival_month > 11.50 | | | | | | |--- arrival_date <= 14.50 | | | | | | | |--- weights: [8.20, 3.04] class: 0 | | | | | | |--- arrival_date > 14.50 | | | | | | | |--- weights: [11.18, 31.88] class: 1 | | | | |--- market_segment_type_Offline > 0.50 | | | | | |--- weights: [112.58, 7.59] class: 0 | |--- avg_price_per_room > 100.04 | | |--- arrival_month <= 11.50 | | | |--- no_of_special_requests <= 2.50 | | | | |--- weights: [0.00, 3200.19] class: 1 | | | |--- no_of_special_requests > 2.50 | | | | |--- weights: [23.11, 0.00] class: 0 | | |--- arrival_month > 11.50 | | | |--- no_of_special_requests <= 0.50 | | | | |--- weights: [35.04, 0.00] class: 0 | | | |--- no_of_special_requests > 0.50 | | | | |--- arrival_date <= 24.50 | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | |--- arrival_date > 24.50 | | | | | |--- weights: [3.73, 22.77] class: 1
importances = best_model.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
Lead time and the Online market Segment are the most important features of the psot-pruned tree.
# training performance comparison
models_train_comp_df = pd.concat(
[
decision_tree_perf_train.T,
decision_tree_tune_perf_train.T,
decision_tree_post_perf_train.T,
],
axis=1,
)
models_train_comp_df.columns = [
"Decision Tree sklearn",
"Decision Tree (Pre-Pruning)",
"Decision Tree (Post-Pruning)",
]
print("Training performance comparison:")
models_train_comp_df
Training performance comparison:
| Decision Tree sklearn | Decision Tree (Pre-Pruning) | Decision Tree (Post-Pruning) | |
|---|---|---|---|
| Accuracy | 0.99421 | 0.83089 | 0.89721 |
| Recall | 0.98661 | 0.78620 | 0.90207 |
| Precision | 0.99578 | 0.72404 | 0.80814 |
| F1 | 0.99117 | 0.75384 | 0.85253 |
# testing performance comparison
models_test_comp_df = pd.concat(
[
decision_tree_perf_test.T,
decision_tree_tune_perf_test.T,
decision_tree_post_test.T,
],
axis=1,
)
models_test_comp_df.columns = [
"Decision Tree sklearn",
"Decision Tree (Pre-Pruning)",
"Decision Tree (Post-Pruning)",
]
print("Test set performance comparison:")
models_test_comp_df
Test set performance comparison:
| Decision Tree sklearn | Decision Tree (Pre-Pruning) | Decision Tree (Post-Pruning) | |
|---|---|---|---|
| Accuracy | 0.86971 | 0.83497 | 0.86778 |
| Recall | 0.80835 | 0.78336 | 0.85576 |
| Precision | 0.79304 | 0.72758 | 0.76401 |
| F1 | 0.80062 | 0.75444 | 0.80729 |
Key Insights:
Conclusions:
Our objective, as well as the company's objective, is to maximize the F1 score, which reflects our aim to minimize false negatives and false positives.
When comparing the decision tree models to the final logistic regression model, we find that the decision tree models achieve a higher F1 score.
Specifically, the decision tree model with post-pruning demonstrates the highest F1 score of approximately 81% on the test set.
If the company prioritizes a simpler and more generalized model, the pre-pruned decision tree may be preferred. This model offers easier interpretability, and the score values for both the training and testing datasets are closely aligned. Additionally, it yields a higher F1 score of around 75% compared to the logistic regression model, which stands at approximately 70%.
However, my recommendation for the company is to adopt the post-pruned decision tree model (best_model). This model effectively mitigates overfitting and achieves the highest F1 score on the test data, aligning with our primary goal.
Recommendations:
To minimize the occurrence of booking cancellations and no-shows at their hotels, INN Hotel groups should consider implementing the following strategies:
Implement a clear and visible cancellation policy: Ensure that guests are aware of the hotel's cancellation policy, including any deadlines for cancellations. For instance, a policy could specify a 24-hour notice period, after which a fee may be charged. This allows canceled bookings to be reassigned to other potential customers.
Offer discounts for confirmed bookings: Incentivize guests to confirm their bookings by providing discounts or special offers. This encourages guests to commit to their reservations, reducing the likelihood of cancellations.
Send booking reminders: Regularly communicate with guests to remind them about their upcoming bookings. These reminders can help guests to plan and remember their reservations, reducing the chance of last-minute cancellations or no-shows.
Rewards program: Incentivize the repeated guests who have consistently low cancellation rates. By displaying that there are benefits for showing loyalty, it will strengthen the brand reputation with the public, which will attract more customers and decrease cancellation rates.
By implementing these measures, the hotels can effectively minimize losses associated with booking cancellations.